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Preoperative opioid use has been reported to be associated with higherpreoperative opioid demand, worse postoperative outcomes, and increasedpostoperative healthcare utilization and expenditures. Understanding the riskof preoperative opioid use helps establish patient-centered pain management. Inthe field of machine learning, deep neural network (DNN) has emerged as apowerful means for risk assessment because of its superb prediction power;however, the blackbox algorithms may make the results less interpretable thanstatistical models. Bridging the gap between the statistical and machinelearning fields, we propose a novel Interpretable Neural Network Regression(INNER), which combines the strengths of statistical and DNN models. We use theproposed INNER to conduct individualized risk assessment of preoperative opioiduse. Intensive simulations and an analysis of 34,186 patients expecting surgeryin the Analgesic Outcomes Study (AOS) show that the proposed INNER not only canaccurately predict the preoperative opioid use using preoperativecharacteristics as DNN, but also can estimate the patient specific odds ofopioid use without pain and the odds ratio of opioid use for a unit increase inthe reported overall body pain, leading to more straightforward interpretationsof the tendency to use opioids than DNN. Our results identify the patientcharacteristics that are strongly associated with opioid use and is largelyconsistent with the previous findings, providing evidence that INNER is auseful tool for individualized risk assessment of preoperative opioid use.

Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression